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by io.github.pipeworx-io

Polymarket Edge Tracker

polymarket_edge_tracker
Read-onlyIdempotent

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage), not intraday.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback in days (default 14, clamp 2-30).
windowNoWhich polymarket_edges window family to read snapshots for: 24hr | 1wk | 1mo (default 1wk).

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds significant behavioral context beyond annotations: it details the output structure (tracked, expired, snapshot_dates), computation of decay and trend, sign conventions, and limitations (TTL, cache-miss behavior). Annotations only state idempotence and read-only, which are supported.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively long but well-structured with clear sections. It is front-loaded with purpose, and each sentence adds value. Slight verbosity could be trimmed, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description fully explains the return format and fields. It covers limitations and edge cases, making it complete for an AI to understand usage and results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with parameter descriptions. The description adds defaults and constraints (clamp, window families) and explains the meaning of window in context of snapshot families, enhancing the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides edge persistence and decay telemetry built from daily snapshots, answering a specific question about edge duration. It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool (to assess edge persistence and decay) and provides concrete examples (fresh vs. old wide edge). It does not explicitly list when not to use or alternatives, but the context is clear enough for an AI to decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes, particularly the ask_pipeworx variants (standard, beta, grounded) and the Polymarket analysis tools (bet_research, polymarket_edges, polymarket_arbitrage). While descriptions help differentiate, an agent may still select the wrong tool for a given task.

Naming Consistency3/5

Names mix verb-initial (ask_pipeworx, compare_entities) and noun-initial (dataset_info, pipeworx_feedback, polymarket_arbitrage) patterns. The snake_case convention is consistent, but the lack of a uniform verb_noun pattern reduces predictability.

Tool Count3/5

With 34 tools, the server is overloaded relative to a clear scope. Many tools are meta-tools (memory, subscription management, feedback) that inflate the count. A more focused set of 15-20 tools would be more coherent.

Completeness4/5

The tool surface covers a wide range of domains: structured data queries, entity profiles, comparisons, prediction market analysis, memory, subscriptions, and SNCF-specific data. Minor gaps exist (e.g., no dedicated weather or sports tools), but the universal ask_pipeworx compensates. Overall, users can accomplish most tasks without dead ends.

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